Unveiling the impact of digital financial inclusion and financial development on global unemployment: A Bayesian quantile regression approach

This study investigates the impact of Digital Financial Inclusion (DFI) and Financial Development (FD) on Unemployment (UNE) across 112 countries from 2004 to 2022. Using the Bayesian Quantile Regression (BQR) method, the analysis reveals that DFI significantly reduces unemployment rates at all quantiles, including 0.1, 0.25, 0.5, 0.75, and 0.9. These findings suggest that DFI has a consistent and positive effect on lowering unemployment across different levels, making it an effective tool for tackling unemployment globally. In contrast, the impact of FD on unemployment is more nuanced. The study shows that FD reduces unemployment at the lower quantiles (0.1 and 0.25), but its effect turns negative at higher quantiles (0.5, 0.75, and 0.9). This indicates that while financial development may have a beneficial effect in countries with lower unemployment rates, its impact becomes less favorable or even exacerbates unemployment in countries with higher unemployment rates. These results suggest that focusing on expanding digital financial inclusion, rather than emphasizing traditional financial development, could be a more effective strategy for reducing unemployment, especially in countries with higher unemployment levels. The study recommends that policymakers prioritize digital financial inclusion as a means to enhance financial access and inclusivity, thus contributing to greater employment opportunities and reduced unemployment in the long run.